Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ZJU-REAL/Easel --skill infographicgit clone --depth 1 https://github.com/ZJU-REAL/EaselWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zju-real/easel/infographic)<a href="https://agentmods.dev/skills/zju-real/easel/infographic"><img src="https://agentmods.dev/badge/skills/zju-real/easel/infographic/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zju-real/easel/infographic"><img src="https://agentmods.dev/badge/skills/zju-real/easel/infographic.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00104 | $0.01280 |
| Opus 5 | $0.00052 | $0.00640 |
| Sonnet 5 | $0.00021 | $0.00256 |
| Haiku 4.5 | $0.00010 | $0.00128 |
Grade A, and why
infographic scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
信息图制作
将数据或文字内容转化为可视化信息图,支持静态 AntV 信息图和动画 GIF 两种输出模式
与其他图表 SKILL 的区别
都能"生成图表",但机制与产物不同,按需求路由:
- infographic(本 SKILL) = 本地渲染。两种产物:静态信息图(AntV DSL,列表/流程/对比/层级 HTML→SVG)+ GIF 动画图表(matplotlib 逐帧→GIF)。
- chart-visualization = 调 AntV 远程 API,产出单张静态图片 URL(25+ 类型),最快拿到单图。
- data-report = 输入 CSV/Excel/JSON,产出整页可视化报告(KPI 卡 + 多图 + 洞察 + 表格)。
两模式边界:要静态矢量信息图(可导出 SVG、模板丰富)→ 模式 A;要会动的 GIF(发社媒/朋友圈的动图,如条形竞赛、数字滚动、进度动画、折线生长)→ 模式 B。
输入
用户提供的文字内容、数据、或主题描述。可以是结构化数据(CSV/JSON)、自然语言描述、或简单的数字罗列。
输出
- 静态模式:
outputs/主题名/infographic.html(浏览器打开,可导出 SVG) - 动画模式:
outputs/主题名/chart.gif(scripts/gif_chart.py直接产出可发社媒的 GIF)
执行步骤
第一步:确认输出模式
询问用户选择输出格式:
- 静态信息图(AntV Infographic)— 列表、流程、对比、层级、关系图等 50+ 模板,矢量渲染,可导出 SVG
- 动画 GIF(
scripts/gif_chart.py)— 条形竞赛 / 数字滚动 / 进度 / 折线生长 4 类爆款动画,直接产出 GIF
如用户需求明确(如"做个条形竞赛动图"或"做个流程图"),直接选择对应模式,无需确认。
第二步:分析内容与选择图表
分析用户输入,提取关键信息结构(标题、描述、数据项等)。选择合适的模板/图表类型。
关键:必须尊重用户输入的语言。用户用中文输入,所有文本必须是中文。
第三步:渲染
-
模式 A(静态 AntV) → 读
references/antv-templates.md:DSL 语法规则、模板选择指南、可用模板列表、HTML 渲染模板。生成 HTML 保存到outputs/,告知路径。 -
模式 B(动画 GIF) → 调
scripts/gif_chart.py <子命令>,无需手写动画代码。子命令按图型:子命令 用途 数据 JSON 结构 bar-race条形竞赛(排名随时间变化,数据可视化爆款) {"title","times":[...],"series":{"名称":[数值×时间]}}count-up数字滚动增长(KPI 从 0 涨到目标) {"title","items":[{"label","value","suffix"}]}或{"label","value"}progress进度动画( --style ring/bar){"label","value","max","color"}line-grow折线逐步生长 {"title","x":[...],"series":{"名称":[数值]}}通用参数:
--output x.gif、--data f.json(-读 stdin,省略用内置示例)、--title、--width(默认 900)、--height、--fps(默认 20)、--duration(秒,默认 4)。典型调用(先写数据到临时 JSON,再调脚本):
python skills/openclaw/infographic/scripts/gif_chart.py bar-race --data data.json \ --output outputs/城市增长/柱状竞速.gif --width 900 --duration 5脚本自动设置中文字体、Agg 后端、自适应调色板控体积。自检:
python skills/openclaw/infographic/scripts/gif_chart.py --selftest。
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago Changed c94d5d670de9
- 10d ago First seen · 80 lines · 104 tokens per session scan A ed49e456b8b1
infographic is a skill published in the GitHub repository ZJU-REAL/Easel (494 stars, last pushed 2d ago), licensed Apache-2.0. It adds 104 tokens to every session and 1,280 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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